Information retrieval systems, as exemplified by Internet search engines, are generally capable of quickly providing documents that are generally relevant to a user's query. Search engines may use a variety of statistical measures of term and document frequency, along with linkages between documents and between terms to determine the relevance of document to a query. A key technical assumption underlying most search engine designs is that a user query accurately represents the user's desired information goal.
In fact, users typically have difficulty formulating good queries. Often, a single query does not provide desired results, and users frequently enter a number of different queries about the same topic. These multiple queries will typically include variations in the breadth or specificity of the query terms, guessed names of entities, variations in the order of the words, the number of words, and so forth, sometimes forming long chains of queries before reaching the desired result set. Because different users have widely varying abilities to successfully revise their queries, various automated methods of query revision have been proposed.
Most commonly, query refinement is used to automatically generate more precise (i.e., narrower) queries from a more general query. Query refinement is primarily useful when users enter over-broad queries whose top results include a superset of documents related to the user's information needs. For example, a user wanting information on the Mitsubishi Galant automobile might enter the query “Mitsubishi,” which is overly broad, as the results will cover the many different Mitsubishi companies, not merely the automobile company. Thus, refining the query would be desirable (though difficult here because of the lack of additional context to determine the specific information need of the user).
However, query refinement is not useful when users enter overly specific queries, where the right revision is to broaden the query, or when the top results are unrelated to the user's information needs. For example, the query “Mitsubishi Galant information” might lead to poor results (in this case, too few results about the Mitsubishi Galant automobile) because of the term “information.” In this case, the right revision is to broaden the query to “Mitsubishi Galant.” Thus, while query refinement works in some situations, there are a large number of situations where a user's information needs are best met by using other query revision techniques.
Another query revision strategy uses synonym lists or thesauruses to expand the query to capture a user's potential information need. As with query refinement, however, query expansion is not always the appropriate way to revise the query, and the quality of the results is very dependent on the context of the query terms.